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Cluster analysis of finnish car retail and service business operations strategy and innovation management capabilities

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Jari Jussila
Jari Jussila

Cluster analysis of Finnish car retail and service business operations strategy and innovation management capabilities. EurOMA 2016 presentation.

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Cluster analysis of Finnish car retail and service business operations strategy and innovation management capabilities Olli Rouvari, Pasi L. Porkka, Heli Aramo-Immonen* heli.aramo-immonen@tut.fi Tampere University of Technology, Pori Unit Mikko Huhtala Autoalan Keskusliitto ry, Finnish Central Organization for Motor Trades and Repairs
RQ •  This research was conducted in order to explore the •  strategic management of operations and •  innovation capability in the Finnish car retail and service business •  The primary goal of the data analysis was to find out whether there existed clusters among the respondents, which could help separate organizations with a good level of strategic management from those with a lower level 19/06/16 2
Research area •  Access to managers was facilitated via the Finnish Central Organization for Motor Trades and Repairs and covered all member companies (147 companies). •  This study gave a good overview of this industry in Finland. •  Of these companies, –  70 % had a turnover of between 5-50 M Eur and –  27% had a turnover of more than 50 M Eur. •  We obtained responses from 37 company managers at a response rate of 25.2%. 19/06/16 3
19/06/16 4http://www.aut.fi/en/statistics/automobile_sector_in_finland/ employed_persons_by_automobile_sector New Car Registration
19/06/16 5 Car Taxes in Finland http://www.aut.fi/en/statistics/taxation_and_car_prices/ price_formation_of_new_passenger_car
Strategic management •  Competitive strategy (Porter, 1985) •  Resource-based view (RBV) (Penrose, 1959; Barney, 1991; Conner, 1991) •  Knowledge-based view (KBV) (e.g. Kaplan and Norton, 1992; Teece, 2002; Sveiby, 2001; Kong, 2008) •  Operative strategy analysis SWOT (Weirich, 1982) 19/06/16 6
Innovation management •  Knowledge creation fuels innovation (Takeuchi, 2013) •  Tidd and Bessant (2009) introduce four types of innovation: process, product/service, positioning and paradigm innovations. 19/06/16 7
Methodology •  Survey questionnaire of 110 questions •  Conducted on the car retail and service business in Finland •  Among 147 CEOs and top managers. •  Obtained responses from 37 company managers •  Response rate of 25.2% •  Statistical analysis methods 19/06/16 8
Methods •  Cluster analysis with all 24 variables revealed no significant clustering among the data. → reduction of variables with factory analysis •  Exploratory factor analysis (EFA) was used for data reduction –  The Kaiser-Meyr-Olkin (KMO) measure was 0.603. –  We used Oblimin rotation with Kaiser Normalization –  Scree test for deciding the number of factors –  Five factors, with total variance explained 71,12% 19/06/16 9
Methods •  Next we calculated values for each factor for each respondent with rotated factor loadings greater than 0.5 •  We employed these five factors as variables and performed a cluster analysis. 19/06/16 10 Cluster # 7 Clusters 6 Clusters 5 Clusters 4 Clusters 3 Clusters 1 1 1 1 1 1 2 1 1 1 1 1 3 1 1 1 4 35 4 3 3 3 31 5 2 2 31 6 10 29 7 19 N = 37 37 37 37 37
Result •  The values in the cluster with 19 respondents were significantly higher in most statements and included differentiating factors. •  Therefore, one can identify the factors that the companies in the lower cluster should improve. •  This distinction into two major clusters with the use of 24 strategic statements also applied to 40 innovation statements. 19/06/16 11
Result •  When the answers to the latter were clustered accordingly, the differences between the clusters were statistically significant. •  This implies that there is a clear connection or correlation between strategic management and innovation management in the companies involved. 19/06/16 12
19/06/16 13 KMO and Bartlett's Test Structure Matrix KMO Measure of Sampling Adequacy .603 Factor Bartlett´s test Appr. Chi-Square 688.354 1 2 3 4 5 of Sphericity df .276 V3 .962 Sig. .000 V4 .794 .500 V2 .664 .538 V1 .605 Total Variance Explained V5 .596 .528 Factor Initial Eigenvalues V15 Total Var. % Cum. % V16 .986 1 8.118 33.823 33.823 V17 .641 .601 2 3.514 14.641 48.465 V19 .534 3 2.268 9.450 57.915 V22 .926 4 1.788 7.451 65.366 V20 .755 5 1.524 6.350 71.715 V21 .737 V23 .731 -.613 V18 .563 .510 Factor Correlation Matrix V24 Factor 1 2 3 4 5 V12 -.907 1 1.000 .027 .174 -.266 .279 V13 -.905 2 .027 1.000 .212 .030 .149 V14 .563 -.730 3 .174 .212 1.000 -.278 .147 V11 -.713 4 -.266 .030 -.278 1.000 -.186 V7 -.546 5 .279 .149 .147 -.186 1.000 V9 .723 Extraction Method: Maximum Likelihood V8 -.600 .700 Rotation: Oblimin with Kaiser Normalization V10 .648 V6 Extraction: Maximum Likelihood Rotation: Oblimin with Kaiser Norm.
19/06/16 14
Conclusions •  The strategy was not communicated to all employees •  Attempts among managers to gain commitment from employees were not efficient •  Collaboration between companies would allow joint resource allocation, which would enable companies to focus on their core competencies 19/06/16 15
Further research areas •  Does strategic and innovative fit indicate smart social media use in a company? 19/06/16 16 http://www.slideshare.net/ jjussila/does-strategic-and- innovative-fit-indicate- smart-social-media-use-in- a-company? qid=5c401802-6083-4997- bf2a-58ca015446c7&v=&b =&from_search=5 IFKAD 2016
Contact! Heli Aramo-Immonen * heli.aramo-immonen@tut.fi Tampere University of Technology Pasi L. Porkka, Olli Rouvari Tampere University of Technology, Pori Unit Mikko Huhtala Autoalan Keskusliitto ry, Finnish Central Organization for Motor Trades and Repairs

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Cluster analysis of finnish car retail and service business operations strategy and innovation management capabilities

  • 1. Cluster analysis of Finnish car retail and service business operations strategy and innovation management capabilities Olli Rouvari, Pasi L. Porkka, Heli Aramo-Immonen* heli.aramo-immonen@tut.fi Tampere University of Technology, Pori Unit Mikko Huhtala Autoalan Keskusliitto ry, Finnish Central Organization for Motor Trades and Repairs
  • 2. RQ •  This research was conducted in order to explore the •  strategic management of operations and •  innovation capability in the Finnish car retail and service business •  The primary goal of the data analysis was to find out whether there existed clusters among the respondents, which could help separate organizations with a good level of strategic management from those with a lower level 19/06/16 2
  • 3. Research area •  Access to managers was facilitated via the Finnish Central Organization for Motor Trades and Repairs and covered all member companies (147 companies). •  This study gave a good overview of this industry in Finland. •  Of these companies, –  70 % had a turnover of between 5-50 M Eur and –  27% had a turnover of more than 50 M Eur. •  We obtained responses from 37 company managers at a response rate of 25.2%. 19/06/16 3
  • 5. 19/06/16 5 Car Taxes in Finland http://www.aut.fi/en/statistics/taxation_and_car_prices/ price_formation_of_new_passenger_car
  • 6. Strategic management •  Competitive strategy (Porter, 1985) •  Resource-based view (RBV) (Penrose, 1959; Barney, 1991; Conner, 1991) •  Knowledge-based view (KBV) (e.g. Kaplan and Norton, 1992; Teece, 2002; Sveiby, 2001; Kong, 2008) •  Operative strategy analysis SWOT (Weirich, 1982) 19/06/16 6
  • 7. Innovation management •  Knowledge creation fuels innovation (Takeuchi, 2013) •  Tidd and Bessant (2009) introduce four types of innovation: process, product/service, positioning and paradigm innovations. 19/06/16 7
  • 8. Methodology •  Survey questionnaire of 110 questions •  Conducted on the car retail and service business in Finland •  Among 147 CEOs and top managers. •  Obtained responses from 37 company managers •  Response rate of 25.2% •  Statistical analysis methods 19/06/16 8
  • 9. Methods •  Cluster analysis with all 24 variables revealed no significant clustering among the data. → reduction of variables with factory analysis •  Exploratory factor analysis (EFA) was used for data reduction –  The Kaiser-Meyr-Olkin (KMO) measure was 0.603. –  We used Oblimin rotation with Kaiser Normalization –  Scree test for deciding the number of factors –  Five factors, with total variance explained 71,12% 19/06/16 9
  • 10. Methods •  Next we calculated values for each factor for each respondent with rotated factor loadings greater than 0.5 •  We employed these five factors as variables and performed a cluster analysis. 19/06/16 10 Cluster # 7 Clusters 6 Clusters 5 Clusters 4 Clusters 3 Clusters 1 1 1 1 1 1 2 1 1 1 1 1 3 1 1 1 4 35 4 3 3 3 31 5 2 2 31 6 10 29 7 19 N = 37 37 37 37 37
  • 11. Result •  The values in the cluster with 19 respondents were significantly higher in most statements and included differentiating factors. •  Therefore, one can identify the factors that the companies in the lower cluster should improve. •  This distinction into two major clusters with the use of 24 strategic statements also applied to 40 innovation statements. 19/06/16 11
  • 12. Result •  When the answers to the latter were clustered accordingly, the differences between the clusters were statistically significant. •  This implies that there is a clear connection or correlation between strategic management and innovation management in the companies involved. 19/06/16 12
  • 13. 19/06/16 13 KMO and Bartlett's Test Structure Matrix KMO Measure of Sampling Adequacy .603 Factor Bartlett´s test Appr. Chi-Square 688.354 1 2 3 4 5 of Sphericity df .276 V3 .962 Sig. .000 V4 .794 .500 V2 .664 .538 V1 .605 Total Variance Explained V5 .596 .528 Factor Initial Eigenvalues V15 Total Var. % Cum. % V16 .986 1 8.118 33.823 33.823 V17 .641 .601 2 3.514 14.641 48.465 V19 .534 3 2.268 9.450 57.915 V22 .926 4 1.788 7.451 65.366 V20 .755 5 1.524 6.350 71.715 V21 .737 V23 .731 -.613 V18 .563 .510 Factor Correlation Matrix V24 Factor 1 2 3 4 5 V12 -.907 1 1.000 .027 .174 -.266 .279 V13 -.905 2 .027 1.000 .212 .030 .149 V14 .563 -.730 3 .174 .212 1.000 -.278 .147 V11 -.713 4 -.266 .030 -.278 1.000 -.186 V7 -.546 5 .279 .149 .147 -.186 1.000 V9 .723 Extraction Method: Maximum Likelihood V8 -.600 .700 Rotation: Oblimin with Kaiser Normalization V10 .648 V6 Extraction: Maximum Likelihood Rotation: Oblimin with Kaiser Norm.
  • 15. Conclusions •  The strategy was not communicated to all employees •  Attempts among managers to gain commitment from employees were not efficient •  Collaboration between companies would allow joint resource allocation, which would enable companies to focus on their core competencies 19/06/16 15
  • 16. Further research areas •  Does strategic and innovative fit indicate smart social media use in a company? 19/06/16 16 http://www.slideshare.net/ jjussila/does-strategic-and- innovative-fit-indicate- smart-social-media-use-in- a-company? qid=5c401802-6083-4997- bf2a-58ca015446c7&v=&b =&from_search=5 IFKAD 2016
  • 17. Contact! Heli Aramo-Immonen * heli.aramo-immonen@tut.fi Tampere University of Technology Pasi L. Porkka, Olli Rouvari Tampere University of Technology, Pori Unit Mikko Huhtala Autoalan Keskusliitto ry, Finnish Central Organization for Motor Trades and Repairs